Is it worth it to personalize the treatment of PTSD? - A variance-ratio meta-analysis and estimation of treatment effect heterogeneity in RCTs of PTSD

Predicting the outcome of psychological treatments for borderline personality disorder and posttraumatic stress disorder: a machine learning approach to predict long-term outcome of Narrative Exposure Therapy vs. Dialectical Behavioral Therapy based treatment

Background: A comorbidity between Borderline Personality Disorder (BPD) and Posttraumatic Stress Disorder (PTSD) is common, severely disabling, and hard to treat. The choice of an optimal psychotherapy based on patient characteristics remains challenging.Objective: This study develops models to predict the outcome of two psychotherapies for comorbid BPD and PTSD.Method: Data from two trials comparing Narrative Exposure Therapy (NET, N = 40) with Dialectical Behavior Therapy-based treatment (DBT-bt, N = 40) was analysed. A cross-validated genetic algorithm was used to detect baseline predictors of change in PTSD symptoms.Results: In the NET group higher education, more baseline PTSD symptoms, more traumatic experiences, fewer baseline BPD symptoms, and not taking antipsychotic medication predicted better treatment outcome. This model (RMSE = 8.98) outperformed the prediction of PTSD symptom reduction with baseline PTSD symptoms alone (RMSE = 10.07) or with all available predictor variables (RMSE = 12.97). Only more baseline PTSD symptoms were selected to predict a better treatment outcome after DBT-bt. This model (RMSE = 9.41) outperformed the prediction of change in PTSD symptoms with all available predictor variables (RMSE = 14.43).Conclusion: Differences in treatment outcome between NET and DBT-bt may be predictable at baseline, to identify which one of both treatments may be most beneficial for individual patients. The small sample size may restrict the generalizability of the results.

The use of biomarkers as measures of PTSD treatment efficacy and predictors of treatment outcomes: A systematic review

The efficacy of posttraumatic stress disorder (PTSD) treatments might be hampered by individual differences. In order to maximize treatment efficacy in existing and newly developed interventions, controlling for individual variables is essential in treatment research. Given the marked physiological correlates of PTSD, biomarkers represent a promising solution. Throughout the PTSD literature, biomarkers have been used to assess treatment effects and predict treatment outcomes. However, the wide variety of biomarkers studied, along with several conflicting results, hinder researchers' abilities to comprehensively interpret the results reported. This systematic review of the literature aimed to identify and classify all biomarkers used to assess the efficacy of PTSD interventions and identify pre-treatment biomarkers able to predict treatment outcomes. Following PRISMA guidelines, we identified 70 studies that assessed biomarkers sensitivity to treatment effects and 25 that used biomarkers to predict treatment outcomes. Well-established treatments and newly developed protocols were included. The results were classified and interpreted by biomarker type. Indicators of neuroanatomical structures and functions were the most commonly studied biomarkers, followed by markers of cardiac activation and glucocorticoid analytes. Cardiac activation markers, and concretely heart rate reactivity to trauma cues, showed the most consistent findings, serving as a valuable method to assess treatment effects across different populations and treatment modalities. Other biomarkers showed promising trends both as predictors of treatment outcomes and measures of treatment efficacy, although essential methodological differences significantly impacted the comparison across studies.

Using machine learning methods to predict the outcome of psychological therapies for post-traumatic stress disorder: A systematic review

BACKGROUND

A number of treatments are available for post-traumatic stress disorder (PTSD), however, there is currently a lack of data-driven treatment selection and adaptation methods for this condition. Machine learning (ML) could potentially help to improve the prediction of treatment outcomes and enable precision mental healthcare in practice.

OBJECTIVES

To systematically review studies that applied ML methods to predict outcomes of psychological therapy for PTSD in adults (e.g., change in symptoms, dropout rate), and evaluate their methodological rigour.

METHODS

This was a pre-registered systematic review (CRD42022325021), which synthesised eligible clinical prediction studies found across four research databases. Risk of bias was assessed using the PROBAST tool. Study methods and findings were narratively synthesised, and adherence to ML best practice evaluated.

RESULTS

Seventeen studies met the inclusion criteria, including samples derived from experimental and observational study designs. All studies were assessed as having a high risk of bias, notably due to inadequately powered samples and a lack of sample size calculations. Training sample size ranged from N < 36-397. The studies applied a diverse range of ML methods such as decision trees, ensembling and boosting techniques. Five studies used unsupervised ML methods, while others used supervised ML. There was an inconsistency in the reporting of hyperparameter tuning and cross-validation methods. Only one study performed external validation.

CONCLUSIONS

ML has the potential to advance precision psychotherapy for PTSD, but to enable this, ML methods must be applied with greater adherence to best practice guidelines.

PACAP a mediator of inflammation following trauma exposure and mild traumatic brain injury: Differential effects in males and females

Individual differences in systemic responses to trauma exposure, posttraumatic stress disorder (PTSD), and/or mild traumatic brain injury (mTBI) may help account for differential risk of adverse sequalae in females and heterogeneity in pathophysiology, symptoms, and treatment responses. Accordingly, this study investigated sex differences in the association between neuroendocrine (pituitary adenylate cyclase-activating polypeptide [PACAP]) and inflammatory markers following lifespan trauma exposure, PTSD, and mTBI in 71 trauma-exposed veteran and non-veteran males (n = 41) and females (n = 30). Two mediation models were proposed and evaluated, informed by an existing theoretical model. Both mediation models examined elevated PACAP as a key variable that may be associated with elevated inflammatory cytokine interleukin-6 (IL-6). The first model evaluated this effect following psychological trauma exposure and the second following mTBI. Trauma exposure and mTBI accounted for a large proportion of sex differences in PACAP and inflammation independent of the effects of time since the events (M = 8-11 years), PTSD symptom severity and diagnostic status, suggesting potentially long-term impacts of trauma exposure and mTBI on systemic pathophysiological responses regardless of PTSD symptom variations. Specifically, PACAP mediated the relationship between cumulative trauma exposure and IL-6 as well as mTBI history and IL-6, with a stronger mediating effect of PACAP on mTBI (β = 0.352) than trauma exposure (β = 0.149). Sex differences were observed in which males with mTBI histories had significantly elevated PACAP levels (Hedges' g = 0.79) and females with mTBI histories had significantly elevated IL-6 levels (Hedges' g = 1.03). PACAP was uniquely associated with trauma exposure in females (β = 0.56) and mTBI in males (β = 0.35). Conversely, IL-6 was uniquely associated with mTBI in females (β = 0.47-0.61) and trauma exposure in males (β = 0.42-0.54). For both sexes, childhood emotional neglect was uniquely associated with PACAP and inflammation later in life. This study presents preliminary evidence of the association between PACAP and inflammation following both trauma exposure and mTBI, which was differentially related in males and females. Although further study is needed, findings have the potential to help explicate heterogeneous presentations and differential risk of trauma-related pathology and mTBI that could lead to more targeted and effective treatments.

Identifying transdiagnostic traumatic stress reactions in U.S. military veterans: A nationally representative study

Traumatic stress reactions (TSRs) exist on a continuum that includes posttraumatic stress disorder (PTSD), highly comorbid psychopathology, and resilience, highlighting the need for comprehensive and integrative approaches capable of capturing the full spectrum of heterogeneous reactions. Here, we used a transdiagnostic and multidimensional method to characterize clinical phenotypes of TSRs in a nationally representative sample of U.S. military veterans. The Middle-Out Approach was used to evaluate self-reported PTSD, generalized anxiety, major depressive symptoms, and physical and mental functioning to identify discrete latent classes of TSRs and their demographic, military and trauma history, and psychosocial correlates. Cross-sectional data were analyzed from 3,727 U.S. veterans who participated in the National Health and Resilience in Veterans Study. Latent class analysis identified five classes of veterans: low TSR (61.3%), anxious/depressive (16.6%), avoidant arousal (9.2%), dysphoric arousal (8.2%), and high TSR (4.7%). Veterans in the dysphoric arousal and high TSR classes demonstrated lower functioning than other classes, which showed similar levels of moderate-to-high functioning despite symptom differences. Classes distinguished between resilience to PTSD symptoms versus resilience to all symptoms and functioning domains and were differentially associated with demographic characteristics, trauma and military histories, and psychosocial characteristics. The results suggest that veterans exhibit different clinical phenotypes of TSRs, which may help inform etiology, diagnostic subtypes, and personalized treatment. Further, although most veterans with psychopathology experience functional impairment, a sizable subset demonstrates high functioning despite psychopathology symptoms.

Correcting for Differences in Measurement Unreliability in Meta-Analysis of Variances

There is a growing interest of researchers in meta-analytic methods for comparing variances as a means to answer questions on between-group differences in variability. When measurements are fallible, however, the variance of an outcome reflects both the variance of the true scores and the error variance. Consequently, effect sizes based on variances, such as the log variability ratio (lnVR) or the log coefficient of variation ratio (lnCVR), may thus not only reflect between-group differences in the true-score variances but also differences in measurement reliability. In this article, we derive formulas to correct the lnVR and lnCVR and their sampling variances for between-group differences in reliability and evaluate their performance in simulation studies. We find that when the goal is to meta-analyze differences between the true-score variances and reliability differs between groups, our proposed corrections lead to accurate estimates of effect sizes and sampling variances in single studies, accurate estimates of the average effect and the between-study variance in random-effects meta-analysis, and adequate type I error rates for the significance test of the average effect. We discuss how to deal with problems arising from missing or imprecise group-specific reliability estimates in meta-analytic data sets and identify questions for further methodological research.

PACAP associated with precise PTSD and fear extinction response in women

Behavioral, biological, and physiological reactions following posttraumatic stress disorder (PTSD) are heterogeneous, particularly between sexes. Pituitary adenylate cyclase-activating polypeptide (PACAP38) is identified as a viable sex-specific marker of PTSD and fear conditioning impairments in women. However, no studies have examined the association between PACAP38 and fear extinction in humans to inform treatment mechanisms, and the association between PACAP38 and PTSD is variable, requiring further investigation. Participants (n = 123) included representative proportions of women (48.8 %), those with ≥subthreshold PTSD (39.8 %), veterans (33.3 %), and participants of color (59.5 %). Main outcomes and measures included PTSD symptoms (CAPS-IV), peripheral serum PACAP38, differential skin conductance response during a fear conditioning paradigm. The Middle-Out Approach was applied to integrate behavioral, biological, and physiological indicators and identify precise clinical phenotypes using latent class analysis. The current study provides behavioral, biological, and physiological evidence of a homogeneous subgroup (13.8 %), composed largely of women, for whom peripheral PACAP38 levels were over twofold higher than other participants (ηp2=.52-.56) and associated with a unique constellation of Intrusive-Hypervigilant PTSD symptoms and impairments in fear extinction retention. Results suggest specificity in the association between PACAP38, PTSD symptoms, and fear extinction retention that can inform practical targets for clinical assessment and intervention, and create viable avenues for future research.

Is personalization of psychological pain treatments necessary? Evidence from a Bayesian variance ratio meta-analysis

ABSTRACT

This is the first study to empirically determine the potential for data-driven personalization in the context of chronic primary pain (CPP). Effect sizes of psychological treatments for individuals with CPP are small to moderate on average. Aiming for better treatment outcomes for the individual patient, the call to personalize CPP treatment increased over time. However, empirical evidence that personalization of psychological treatments can optimize treatment outcomes in CPP is needed. This study seeks to estimate heterogeneity of treatment effect for cognitive behavioral therapy (CBT) as the psychological treatment approach for CPP with the greatest evidence base. For this purpose, a Bayesian variance ratio meta-regression is conducted using updated data from 2 recently published meta-analyses with randomized controlled trials comparing CBT delivered face-to-face to treatment-as-usual or waiting list controls. Heterogeneity in patients with CPP would be reflected by a larger overall variance in the post-treatment score compared with the control group. We found first evidence for an individual treatment effect in CBT compared with the control group. The estimate for the intercept was 0.06, indicating a 6% higher variance of end point values in the intervention groups. However, this result warrants careful consideration. Further research is needed to shed light on the heterogeneity of psychological treatment studies and thus to uncover the full potential of data-driven personalized psychotherapy for patients with CPP.A Bayesian variance ratio meta-regression indicates empirical evidence that data-driven personalized psychotherapy for patients with chronic primary pain could increase effects of cognitive behavioral therapy.

Advancing the personalized advantage index (PAI): a systematic review and application in two large multi-site samples in anxiety disorders

BACKGROUND

The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

METHODS

Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

RESULTS

The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

CONCLUSION

Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.

Treatment effect heterogeneity of cognitive behavioral therapy for insomnia - A meta-analysis

Investigation of the heterogeneity of the treatment effect (HTE) might guide the optimization of cognitive behavioral therapy for insomnia (CBT-I). This study examined HTE in CBT-I thereby analyzing if treatment setting, control group, different CBT-I components, and patient characteristics drive HTE. Randomized controlled trials investigating CBT-I were included. Bayesian random effect meta-regressions were specified to examine variances between the intervention and control groups regarding post-treatment symptom severity. Subgroup analyses analyzing treatment setting and control groups and covariate analysis analyzing treatment components and patient characteristics were specified. No significant HTE in CBT-I was found for the overall data set, settings and control groups. The covariate analyses yielded significant results for baseline severity and the treatment component relaxation therapy. Thus, this study identified potential causes for HTE in CBT-I for the first time, showing that it might be worthwhile to further examine possibilities for precision medicine in CBT-I.

Do all individuals benefit equally from non-surgical periodontal therapy? Secondary analyses of systematic review data

AIMS

This study aimed to assess the variability and treatment effect heterogeneity in response to non-surgical periodontal therapy (NSPT).

METHODS

Data from randomized controlled trials included in two recent systematic reviews on the effect of NSPT on mean clinical attachment loss (CAL), mean probing pocket depth (PPD), percentage of sites with bleeding on probing (%BOP), PPD ≤3 mm (%PD ≤3 mm), and C-reactive protein levels (CRP) at 3-12-month follow-up among adults with systemic diseases or conditions were used. In these trials, the control arms received no treatment, hygiene advice, or supragingival scaling. The Bayesian meta-regression models were utilized to assess the variability ratios between NSPT and control groups.

RESULTS

Data from 36 trials on mean PPD, 32 trials on mean CAL, eight trials on %PD ≤3 mm, 31 trials on %BOP and 19 trials on CRP were used. Variability in mean CAL and CRP was approximately 10% higher in the NSPT arms than in the control arms, hinting that there may be room for treatment effect heterogeneity. Instead, variability in mean PPD, %BOP, and %PD ≤3 mm was lower in the NSPT arms than in the control arms.

CONCLUSION

Potential treatment effect heterogeneity in response to NSPT was observed for CRP and mean CAL. However, substantial measurement error in CAL and natural variation in CRP may contribute to these findings. Conversely, treatment effect heterogeneity appears less pronounced for mean PPD, %BOP, and %PD ≤3 mm, potentially due to greater treatment effects in patients with more severe periodontitis and reduced measurement error in these parameters.

Leveraging Single-Case Experimental Designs to Promote Personalized Psychological Treatment: Step-by-Step Implementation Protocol with Stakeholder Involvement of an Outpatient Clinic for Personalized Psychotherapy

Our objective is to implement a single-case experimental design (SCED) infrastructure in combination with experience-sampling methods (ESM) into the standard diagnostic procedure of a German outpatient research and training clinic. Building on the idea of routine outcome monitoring, the SCED infrastructure introduces intensive longitudinal data collection, individual effectiveness measures, and the opportunity for systematic manipulation to push personalization efforts further. It aims to empower psychotherapists and patients to evaluate their own treatment (idiographic perspective) and to enable researchers to analyze open questions of personalized psychotherapy (nomothetic perspective). Organized around the principles of agile research, we plan to develop, implement, and evaluate the SCED infrastructure in six successive studies with continuous stakeholder involvement: In the project development phase, the business model for the SCED infrastructure is developed that describes its vision in consideration of the context (Study 1). Also, the infrastructure's prototype is specified, encompassing the SCED procedure, ESM protocol, and ESM survey (Study 2 and 3). During the optimization phase, feasibility and acceptability are tested and the infrastructure is adapted accordingly (Study 4). The evaluation phase includes a pilot implementation study to assess implementation outcomes (Study 5), followed by actual implementation using a within-institution A-B design (Study 6). The sustainability phase involves continuous monitoring and improvement. We discuss to what extent the generated data could be used to address current questions of personalized psychotherapy research. Anticipated barriers and limitations during the implementation processes are outlined.

Defining and operationalizing personalized psychological treatment - a systematic literature review

This systematic literature review aimed to propose a definition of personalized psychological treatment and to suggest how the definition can be operationalized. PsycINFO, Cochrane Library, MEDLINE and EMBASE were searched up to 11 December 2023 for studies in which a definition of personalized psychological treatment was included or a systematic operationalization of personalized psychological treatment was described. Based on a narrative synthesis of the collected definitions, summary categories were developed that informed the proposed definition. Operationalizations were described according to what aspect of treatment, how and when treatment was personalized. The extent to which the operationalizations deviated from the proposed definition was assessed. Thirty-four studies with definitions and 200 with operationalizations were included. The following definition was proposed: personalized psychological treatment aims to optimize treatment outcome for the individual patient by tailoring treatment to unique or specific needs, preferences or other characteristics and includes a systematic adaptation of treatment or a differentiation between treatment strategies. Based on the operationalizations, timing of personalization, specification of the systematic approach and treatment elements that could be personalized were added to the proposed definition. Evidence-based personalization of psychological treatments can be enhanced by clear operationalization based on a comprehensive definition of personalization.

Evidence for Locus Coeruleus-Norepinephrine System Abnormality in Military Posttraumatic Stress Disorder Revealed by Neuromelanin-Sensitive Magnetic Resonance Imaging

BACKGROUND

The complex neurobiology of posttraumatic stress disorder (PTSD) calls for the characterization of specific disruptions in brain functions that require targeted treatment. One such alteration could be an overactive locus coeruleus (LC)-norepinephrine system, which may be linked to hyperarousal symptoms, a characteristic and burdensome aspect of the disorder.

METHODS

Study participants were Canadian Armed Forces veterans with PTSD related to deployment to combat zones (n = 34) and age- and sex-matched healthy control participants (n = 32). Clinical measures included the Clinician-Administered PTSD Scale for DSM-5, and neuroimaging measures included a neuromelanin-sensitive magnetic resonance imaging scan to measure the LC signal. Robust linear regression analyses related the LC signal to clinical measures.

RESULTS

Compared with control participants, the LC signal was significantly elevated in the PTSD group (t62 = 2.64, p = .010), and this group difference was most pronounced in the caudal LC (t56 = 2.70, Cohen's d = 0.72). The caudal LC signal was also positively correlated with the severity of Clinician-Administered PTSD Scale for DSM-5 hyperarousal symptoms in the PTSD group (t26 = 2.16, p = .040).

CONCLUSIONS

These findings are consistent with a growing body of evidence indicative of elevated LC-norepinephrine system function in PTSD. Furthermore, they indicate the promise of neuromelanin-sensitive magnetic resonance imaging as a noninvasive method to probe the LC-norepinephrine system that has the potential to support subtyping and treatment of PTSD or other neuropsychiatric conditions.

Understanding trauma as contextualized adverse life events that threaten the individual: Commentary in response to Marx et al. (2024)

In this commentary, I propose that a person-oriented and research-focused approach can stimulate the discussion on the definition of a traumatic stressor and help to refine Criterion A in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Particularly, I suggest that a contextual perspective focusing on the interaction between event features and person-related factors captures more adequately the individual perception of and cognitions related to extremely threatening adverse life events for a diversity of individuals exposed to trauma. In future debate, I encourage the involvement of patients and the public and urge consideration of all potential consequences for practice and research that can directly result from changes to Criterion A (e.g., the heterogenization of posttraumatic stress disorder).

The Middle-Out Approach to reconceptualizing, assessing, and analyzing traumatic stress reactions

Alternative models of traumatic stress and broader psychopathology have been proposed to address issues of heterogeneity, comorbidity, clinical utility, and equitable representation. However, systematic and practical methods and guidelines to organize and apply these models remain scarce. The Middle-Out Approach is a novel, integrative, contextually informed framework for organizing and applying existing empirical methods to evaluate current and alternative traumatic stress reactions. Rather than beginning to identify traumatic stress reactions from the top-down (i.e., disorder-first approach) or bottom-up (i.e., symptom-first approach), constructs are evaluated from the middle out (i.e., presentation-first approach), unconstrained by higher-order disorders or lower-order diagnostic symptoms. This approach provides innovation over previous methods at multiple levels, including the conceptualization of traumatic stress reactions as well as the type of assessments and data sources used and how they are used in statistical analyses. Conceptualizations prioritize the identification of middle-order phenotypes, representing person-centered clinical presentations, which are informed by the integration of multidimensional, transdiagnostic, and multimodal (e.g., psychosocial, physiological) assessments and/or data sources. Integrated data are then analyzed concurrently using person-centered statistical models to identify precise, discrete, and representative health outcomes within broader heterogeneous samples. Subsequent variable-centered analyses are then used to identify culturally sensitive and contextually informed correlates of phenotypes, their clinical utility, and the differential composition within and between broader traumatic stress reactions. Examples from the moral injury literature are used to illustrate practical applications that may increase clinical utility and the accurate representation of health outcomes for diverse individuals and communities.

Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model

Precision medicine methods (machine learning; ML) can identify which clients with generalized anxiety disorder (GAD) benefit from mindfulness ecological momentary intervention (MEMI) vs. self-monitoring app (SM). We used randomized controlled trial data of MEMI vs. SM for GAD (N = 110) and tested three ML models to predict one-month follow-up reliable improvement in GAD severity, perseverative cognitions (PC), trait mindfulness (TM), and executive function (EF). Eleven baseline predictors were tested regarding differential reliable change from MEMI vs. SM (age, sex, race, EF errors, inhibitory dyscontrol, set-shifting deficits, verbal fluency, working memory, GAD severity, TM, PC). The final top five prescriptive predictor models of all outcomes performed well (AUC = .752 .886). The following variables predicted better outcome from MEMI vs. SM: Higher GAD severity predicted more GAD improvement but less EF improvement. Elevated PC, inhibitory dyscontrol, and verbal dysfluency predicted better improvement in most outcomes. Greater set-shifting and TM predicted stronger improvements in GAD symptoms and TM. Older age predicted more alleviation of GAD and PC symptoms. Women exhibited more enhancements in trait mindfulness and EF than men. White individuals benefitted more than non-White. PC, TM, EF, and sociodemographic data might help predictive models optimize intervention selection for GAD.

The effect of individual characteristics on susceptibility to aggressive and/or intimidating approaches: quantifying probability pathways by creating a victimization model

Background: A significant body of literature has identified multiple factors that contribute to established victimization by aggressive and/or intimidating behaviours. These studies primarily originate from the fields of intimate partner violence (IPV), bullying, sexual abuse, and/or commercial sexual exploitation (CSE), and generally focus on female victims. It appears, however, complicated to quantify the cumulative contribution of these factors on susceptibility to intimidating and/or hostile engagements on an individual level.Objective: To develop a comprehensive risk model to quantify, on an individual level, the cumulative effects of previously reported characteristics on susceptibility to aggressive/intimidating approaches, leading to victimization (e.g. in the context of IPV/sexual abuse).Methods: A Bayesian belief network was developed using data from previous studies, capturing the multivariate contribution of previously reported characteristics on the likelihood of becoming victimized by aggressive and/or intimidating approaches (e.g. in the IPV/CSE context) in female victims aged 12-24 years.Results: The model showed that specific combinations of characteristics may contribute to an increased likelihood of victimization (e.g. in the context of IPV/bullying/sexual abuse or CSE). This likelihood could be quantified and categorized into specific clusters of factors differentiating between victimization by physically violent, non-physical, and/or sexual aggressive/intimidating approaches.Conclusion: The present model appears to be the first to successfully quantify the cumulative contribution of individual characteristics on the likelihood of becoming victimized by aggressive and/or intimidating approaches, typically leading to victimization. Moreover, the present scientific effort and resulting model suggest that there may be a latent variable mediating between the implemented factors and overall outcome, i.e. the susceptibility to aggressive and/or intimidating approaches. From that perspective, the model may also be considered as an initial outline to effectively indicate susceptibility to such approaches.

Comparing phase-based treatment, prolonged exposure, and skills-training for Complex Posttraumatic Stress Disorder: A randomized controlled trial

OBJECTIVE

This study examines treatment effects in STAIR Narrative Therapy (SNT), a phase-based treatment where Skills Training in Affective and Interpersonal Regulation (STAIR) precedes Narrative Therapy (NT), compared to Prolonged Exposure (PE) and to STAIR.

METHOD

Ninety-two adult patients diagnosed with DSM-5 PTSD and ICD-11 CPTSD following childhood abuse were randomly assigned to enhanced versions of SNT (12 group STAIR sessions + 8 individual NT sessions), PE (8-16 individual sessions), or STAIR (12 group STAIR sessions) provided in residential care. Outcome was assessed by mixed models.

RESULTS

PE produced greater improvements in DSM-5 PTSD symptoms compared to SNT from pre-treatment to post-treatment, but not compared to STAIR. Reductions in ICD-11 CPTSD symptoms were not significantly different among conditions. From pre-treatment to 1 year follow-up, PE produced greater PTSD symptom improvements than SNT and STAIR, and PE and STAIR produced greater CPTSD symptom improvements than SNT.

CONCLUSIONS

The predicted stronger effect of SNT compared to PE and STAIR on DSM-5 PTSD and ICD-11 CPTSD symptoms was not supported by the findings. The benefits of immediate trauma-focused treatments (TFT) as compared to phase-based treatments, and the potential non-inferiority of skills-training as compared to TFT in CPTSD needs to be further investigated.

Using the Personalized Advantage Index to determine which veterans may benefit from more vs. less comprehensive intensive PTSD treatment programs

Background: Intensive PTSD treatment programs (ITPs) are highly effective but tend to differ greatly in length and the number of adjunctive services that are provided in conjunction with evidence-based PTSD treatments. Individuals' treatment response to more or less comprehensive ITPs is poorly understood.Objective: To apply a machine learning-based decision-making model (the Personalized Advantage Index (PAI)), using clinical and demographic factors to predict response to more or less comprehensive ITPs.Methods: The PAI was developed and tested on a sample of 747 veterans with PTSD who completed a 3-week (more comprehensive; n = 360) or 2-week (less comprehensive; n = 387) ITP.Results: Approximately 12.32% of the sample had a PAI value that suggests that individuals would have experienced greater PTSD symptom change (5 points) on the PTSD Checklist for DSM-5 in either a more- or less comprehensive ITP. For individuals with the highest 25% of PAI values, effect sizes for the amount of PTSD symptom change between those in their optimal vs. non-optimal programs was d = 0.35.Conclusions: Although a minority was predicted to have benefited more from a program, there generally was not a substantial difference in predicted outcomes. Less comprehensive and thus more financially sustainable ITPs appear to work well for most individuals with PTSD.

What if we wait? Using synthetic waiting lists to estimate treatment effects in routine outcome data

Objective: Due to the lack of randomization, pre-post routine outcome data precludes causal conclusions. We propose the "synthetic waiting list" (SWL) control group to overcome this limitation.

Method: First, a step-by-step introduction illustrates this novel approach. Then, this approach is demonstrated using an empirical example with data from an outpatient cognitive-behavioral therapy (CBT) clinic (N = 139). We trained an ensemble machine learning model ("Super Learner") on a data set of patients waiting for treatment (N = 311) to make counterfactual predictions of symptom change during this hypothetical period.

Results: The between-group treatment effect was estimated to be d = 0.42. Of the patients who received CBT, 43.88% achieved reliable and clinically significant change, while this probability was estimated to be 14.54% in the SWL group. Counterfactual estimates suggest a clear net benefit of psychotherapy for 41% of patients. In 32%, the benefit was unclear, and 27% would have improved similarly without receiving CBT.

Conclusions: The SWL is a viable new approach that provides between-group outcome estimates similar to those reported in the literature comparing psychotherapy with high-intensity control interventions. It holds the potential to mitigate common limitations of routine outcome data analysis.

Measuring the active elements of cognitive-behavioral therapies

Understanding how and for whom cognitive-behavioral therapies work is central to the development and improvement of mental health interventions. Suboptimal quantification of the active elements of cognitive-behavioral therapies has hampered progress in elucidating mechanisms of change. To advance process research on cognitive-behavioral therapies, we describe a theoretical measurement framework that focuses on the delivery, receipt, and application of the active elements of these interventions. We then provide recommendations for measuring the active elements of cognitive-behavioral therapies aligned with this framework. Finally, to support measurement harmonization and improve study comparability, we propose the development of a publicly available repository of assessment tools: the Active Elements of Cognitive-Behavioral Therapies Measurement Kit.

Examining trauma cognition change trajectories among initial PTSD treatment non-optimal responders: a potential avenue to guide subsequent treatment selection

BACKGROUND

Despite their general effectiveness, 14-50% of individuals do not fully respond to evidence-based treatments for posttraumatic stress disorder (PTSD). Although changes in negative posttrauma cognitions (NPCs) are considered a likely PTSD treatment mechanism, less is known about how NPCs change among individuals who continue to be symptomatic following treatment (non-optimal responders).

OBJECTIVE

The objective of this study was to examine NPC change trajectories among individuals who were determined to be non-optimally responsive to intensive PTSD treatment.

METHOD

Using a 3-week Cognitive Processing Therapy-based intensive PTSD treatment sample (ITP; N = 243), the present study examined the number of distinct NPC change trajectories among non-optimal responders via Group Based Trajectory Modeling and assessed predictors of non-optimal responders' NPC change trajectory membership. Analyses were replicated in a separate 2-week ITP sample (N = 215).

RESULTS

In both non-optimal responder samples, two trajectories emerged; a no NPC change group which represented those with an overall lack of NPC change throughout treatment and an NPC change group which represented those with an overall reduction of NPCs occurring primarily later in treatment. Changes in PTSD symptom severity during treatment was the only consistent predictor of NPC change trajectory group membership among treatment non-optimal responders across ITPs.

CONCLUSIONS

Findings suggest NPC change among non-optimal responders is nuanced and may inform subsequent intervention selection, resulting in testable hypotheses for future research.

Praxisorientierte Forschung in der Psychotherapie

Zusammenfassung. In den letzten Jahrzehnten hat sich durch randomisiert-kontrollierte Studien (RCTs) eine breite Evidenzbasis von Psychotherapie mit mittleren bis großen Effekten für verschiedene psychische Störungen gebildet. Neben der Bestimmung dieser Wirksamkeit („Efficacy“) ebneten Studien zur Wirksamkeit unter alltäglichen Routinebedingungen („Effectiveness“) historisch den Weg zur Entwicklung eines praxisorientierten Forschungsparadigmas. Im Beitrag wird argumentiert, dass im Rahmen dieses Paradigmas praxisbasierte Studien eine wertvolle Ergänzung zu RCTs darstellen, da sie existierende Probleme in der Psychotherapieforschung adressieren können. In der gegenwärtigen praxisorientierten Forschung liefern dabei neue Ansätze aus der personalisierten Medizin und Methoden aus der ‚Computational Psychiatry‘ wichtige Anhaltspunkte zur Optimierung von Effekten in der Psychotherapie. Im Kontext der Personalisierung werden bspw. klinische multivariable Prädiktionsmodelle entwickelt, welche durch Rückmeldeschleifen an Praktiker_innen kurzfristig ein evidenzbasiertes Outcome-Monitoring ermöglicht und langfristig das Praxis-Forschungsnetzwerk in Deutschland stärkt. Am Ende des Beitrags werden zukünftige Richtungen für die praxisorientierte Forschung im Sinne des ‘Precision Mental Health Care’ -Paradigmas abgeleitet und diskutiert.